Data Exploration with Python and Jupyter¶

Basic usage of the Pandas library to download a dataset, explore its contents, clean up missing or invalid data, filter the data according to different criteria, and plot visualizations of the data.

  • Part 1: Python and Jupyter
  • Part 2: Pandas with toy data
  • Part 3: Pandas with real data

Press Spacebar to go to the next slide (or ? to see all navigation shortcuts)

Let's download some real data¶

For some reason, the London Fire Brigade provides a public spreadsheet of all animal rescue incidents since 2009:

https://data.london.gov.uk/dataset/animal-rescue-incidents-attended-by-lfb

They provide a link to the dataset in csv (comma-delimited) format

In [1]:
# import the Pandas library & matplotlib for plotting

import pandas as pd
import matplotlib.pyplot as plt
In [2]:
# download a csv file with some data and convert it to a DataFrame
url = "https://data.london.gov.uk/download/animal-rescue-incidents-attended-by-lfb/01007433-55c2-4b8a-b799-626d9e3bc284/Animal%20Rescue%20incidents%20attended%20by%20LFB%20from%20Jan%202009.csv"
df = pd.read_csv(url)

Suggested workflow / philosophy¶

  • you want to do something
    • if you know / have a guess which function to use, look at its docstring: ?function_name
    • if you don't have any idea what to try, google how do I ... in pandas
    • if in doubt, just try something!
  • if you get an error, copy & paste the last bit into google (along with funtion_name and/or pandas)
    • don't be intimidated by the long and apparently nonsensical error messages
    • almost certainly someone else has had this exact problem
    • almost certainly the solution is waiting for you
  • look for a stackoverflow answer with many up-votes
    • ignore the green tick, this just means the person asking the question liked the answer
    • typically an answer with many up-votes is a better option
    • more recent answers can also be better: sometimes a library has changed since an older answer was written

(For anyone who wasn't already doing this, that may be the most useful thing in this course)

Display the DataFrame¶

In [3]:
df
Out[3]:
IncidentNumber DateTimeOfCall CalYear FinYear TypeOfIncident PumpCount PumpHoursTotal HourlyNotionalCost(£) IncidentNotionalCost(£) FinalDescription ... UPRN Street USRN PostcodeDistrict Easting_m Northing_m Easting_rounded Northing_rounded Latitude Longitude
0 139091 2009-01-01 03:01:00 2009 2008/09 Special Service 1.0 2.0 255 510.0 Redacted ... NaN Waddington Way 20500146.0 SE19 NaN NaN 532350 170050 NaN NaN
1 275091 2009-01-01 08:51:00 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted ... NaN Grasmere Road NaN SE25 534785.0 167546.0 534750 167550 51.390954 -0.064167
2 2075091 2009-01-04 10:07:00 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted ... NaN Mill Lane NaN SM5 528041.0 164923.0 528050 164950 51.368941 -0.161985
3 2872091 2009-01-05 12:27:00 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted ... 1.000215e+11 Park Lane 21401484.0 UB9 504689.0 190685.0 504650 190650 51.605283 -0.489684
4 3553091 2009-01-06 15:23:00 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted ... NaN Swindon Lane 21300122.0 RM3 NaN NaN 554650 192350 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9723 096744-30062023 2023-06-30 15:56:00 2023 2023/24 Special Service 1.0 1.0 388 388.0 Redacted ... 1.000207e+11 SELSDON PARK ROAD 20502647.0 CR2 536269.0 162904.0 536250 162950 51.348885 -0.044629
9724 096880-30062023 2023-06-30 20:21:00 2023 2023/24 Special Service 1.0 1.0 388 388.0 PIGEON STUCK BETWEEN FENCES IN PLAYING FIE... ... 2.070058e+08 CAMBRIDGE GARDENS 20702560.0 EN1 534305.0 197280.0 534350 197250 51.658273 -0.059731
9725 096884-30062023 2023-06-30 20:31:00 2023 2023/24 Special Service 1.0 1.0 388 388.0 CAT STUCK BETWEEN BUILT IN FRIDGE AND WALL ... NaN EASTFIELD ROAD 20702215.0 EN3 NaN NaN 535650 198250 NaN NaN
9726 096913-30062023 2023-06-30 21:24:00 2023 2023/24 Special Service 1.0 2.0 388 776.0 Redacted ... NaN NORBURY COURT ROAD 20501229.0 SW16 NaN NaN 530650 169150 NaN NaN
9727 096935-30062023 2023-06-30 22:26:00 2023 2023/24 Special Service 1.0 1.0 388 388.0 Redacted ... NaN QUEENSHURST SQUARE 21880473.0 KT2 NaN NaN 518150 169750 NaN NaN

9728 rows × 31 columns

Column data types¶

In [4]:
df.dtypes
Out[4]:
IncidentNumber                 object
DateTimeOfCall                 object
CalYear                         int64
FinYear                        object
TypeOfIncident                 object
PumpCount                     float64
PumpHoursTotal                float64
HourlyNotionalCost(£)           int64
IncidentNotionalCost(£)       float64
FinalDescription               object
AnimalGroupParent              object
OriginofCall                   object
PropertyType                   object
PropertyCategory               object
SpecialServiceTypeCategory     object
SpecialServiceType             object
WardCode                       object
Ward                           object
BoroughCode                    object
Borough                        object
StnGroundName                  object
UPRN                          float64
Street                         object
USRN                          float64
PostcodeDistrict               object
Easting_m                     float64
Northing_m                    float64
Easting_rounded                 int64
Northing_rounded                int64
Latitude                      float64
Longitude                     float64
dtype: object

Convert DateTimeOfCall to a date-time¶

In [5]:
df["DateTimeOfCall"].head()
Out[5]:
0    2009-01-01 03:01:00
1    2009-01-01 08:51:00
2    2009-01-04 10:07:00
3    2009-01-05 12:27:00
4    2009-01-06 15:23:00
Name: DateTimeOfCall, dtype: object
In [6]:
# this looks like what we want..
pd.to_datetime(df["DateTimeOfCall"]).head()
Out[6]:
0   2009-01-01 03:01:00
1   2009-01-01 08:51:00
2   2009-01-04 10:07:00
3   2009-01-05 12:27:00
4   2009-01-06 15:23:00
Name: DateTimeOfCall, dtype: datetime64[ns]
In [7]:
# ..but which number is the month and which is the day?
# how can we check if what we just did was correct?
pd.to_datetime(df["DateTimeOfCall"]).plot()
# should be a single monotonically increasing line: looks good!
Out[7]:
<Axes: >
No description has been provided for this image
In [8]:
# replace DateTimeOfCall column in dataframe with this one
df["DateTimeOfCall"] = pd.to_datetime(df["DateTimeOfCall"])

Use the datetime as the index¶

In [9]:
df.set_index("DateTimeOfCall", inplace=True)
In [10]:
df
Out[10]:
IncidentNumber CalYear FinYear TypeOfIncident PumpCount PumpHoursTotal HourlyNotionalCost(£) IncidentNotionalCost(£) FinalDescription AnimalGroupParent ... UPRN Street USRN PostcodeDistrict Easting_m Northing_m Easting_rounded Northing_rounded Latitude Longitude
DateTimeOfCall
2009-01-01 03:01:00 139091 2009 2008/09 Special Service 1.0 2.0 255 510.0 Redacted Dog ... NaN Waddington Way 20500146.0 SE19 NaN NaN 532350 170050 NaN NaN
2009-01-01 08:51:00 275091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Fox ... NaN Grasmere Road NaN SE25 534785.0 167546.0 534750 167550 51.390954 -0.064167
2009-01-04 10:07:00 2075091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Dog ... NaN Mill Lane NaN SM5 528041.0 164923.0 528050 164950 51.368941 -0.161985
2009-01-05 12:27:00 2872091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Horse ... 1.000215e+11 Park Lane 21401484.0 UB9 504689.0 190685.0 504650 190650 51.605283 -0.489684
2009-01-06 15:23:00 3553091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Rabbit ... NaN Swindon Lane 21300122.0 RM3 NaN NaN 554650 192350 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-06-30 15:56:00 096744-30062023 2023 2023/24 Special Service 1.0 1.0 388 388.0 Redacted Cat ... 1.000207e+11 SELSDON PARK ROAD 20502647.0 CR2 536269.0 162904.0 536250 162950 51.348885 -0.044629
2023-06-30 20:21:00 096880-30062023 2023 2023/24 Special Service 1.0 1.0 388 388.0 PIGEON STUCK BETWEEN FENCES IN PLAYING FIE... Bird ... 2.070058e+08 CAMBRIDGE GARDENS 20702560.0 EN1 534305.0 197280.0 534350 197250 51.658273 -0.059731
2023-06-30 20:31:00 096884-30062023 2023 2023/24 Special Service 1.0 1.0 388 388.0 CAT STUCK BETWEEN BUILT IN FRIDGE AND WALL Cat ... NaN EASTFIELD ROAD 20702215.0 EN3 NaN NaN 535650 198250 NaN NaN
2023-06-30 21:24:00 096913-30062023 2023 2023/24 Special Service 1.0 2.0 388 776.0 Redacted Cat ... NaN NORBURY COURT ROAD 20501229.0 SW16 NaN NaN 530650 169150 NaN NaN
2023-06-30 22:26:00 096935-30062023 2023 2023/24 Special Service 1.0 1.0 388 388.0 Redacted Cat ... NaN QUEENSHURST SQUARE 21880473.0 KT2 NaN NaN 518150 169750 NaN NaN

9728 rows × 30 columns

In [11]:
# can now use datetime to select rows: here is jan 2021
df.loc["2021-01-01":"2021-01-31", "FinalDescription"]
Out[11]:
DateTimeOfCall
2021-01-01 12:09:00        KITTEN STUCK UP TREE  AL REQUESTED FROM SCENE
2021-01-01 14:06:00                                             Redacted
2021-01-03 18:40:00                CAT WITH LEG TRAPPED IN BATH PLUGHOLE
2021-01-04 13:39:00                                             Redacted
2021-01-06 10:22:00                                             Redacted
2021-01-06 13:09:00    CAT IN DISTRESS ON ROOF - ADDITIONAL APPLIANCE...
2021-01-06 20:35:00        DOG TRAPPED IN FOX HOLE  - MEET AT CLUB HOUSE
2021-01-07 23:50:00                   KITTEN STUCK BETWEEN WALL AND ROOF
2021-01-09 08:01:00                                  DOG STUCK IN TRENCH
2021-01-10 19:27:00                                             Redacted
2021-01-12 11:39:00                                             Redacted
2021-01-12 22:38:00                                 CAT TRAPPED IN DITCH
2021-01-16 18:05:00                          DOG TRAPPED IN PORTER CABIN
2021-01-17 16:09:00    DOG TRAPPED IN WAREHOUSE AREA - CALLER BELIEVE...
2021-01-17 17:09:00      BIRD TRAPPED IN NETTING    CALLER WILL MEET YOU
2021-01-18 15:17:00            CAT STUCK IN TREE BEING ATTACKED BY CROWS
2021-01-18 17:06:00    ASSIST RSPCA - SMALL ANIMAL RESUE - BIRD ENTAN...
2021-01-19 18:28:00                          CAT TRAPPED BEHIND CUPBOARD
2021-01-19 20:24:00                                             Redacted
2021-01-19 20:36:00                              RUNNING CALL AT ON ROOF
2021-01-20 09:35:00                      CAT STUCK BETWEEN TREE BRANCHES
2021-01-21 13:15:00                              SWAN TRAPPED IN NETTING
2021-01-21 18:23:00                               CAT TRAPPED IN CHIMNEY
2021-01-22 14:22:00                   CAT TRAPPED BETWEEN WALL AND FENCE
2021-01-23 10:18:00                               CAT TRAPPED IN CHIMNEY
2021-01-23 15:43:00                            CAT TRAPPED BETWEEN WALLS
2021-01-23 17:16:00                                             Redacted
2021-01-25 12:02:00             ASSIST RSPCA WITH FOX STUCK DOWN CULVERT
2021-01-26 13:42:00         DOG STUCK IN RAILINGS - CALLER WILL MEET YOU
2021-01-26 18:21:00                                             Redacted
2021-01-26 22:44:00    BIRDS TRAPPED IN BASKETBALL COURT CALLER IS ON...
2021-01-26 23:35:00             FOX TRAPPED IN FENCE IN ALLEYWAY NEXT TO
2021-01-27 09:18:00    CAT STUCK IN TREE - ATTENDED YESTERDAY AND ADV...
2021-01-27 10:12:00    BIRD TRAPPED BY LEG IN A TREE - RSPCA IN ATTEN...
2021-01-27 15:22:00                           CAT UP TREE   ASSIST RSPCA
2021-01-29 10:47:00                 TRAPPED FOX IN FENCE  IN REAR GARDEN
2021-01-30 14:53:00                                 CAT STUCK UNDER SHED
2021-01-30 15:28:00              BIRD CAUGHT IN NETTING - RSPCA ON SCENE
2021-01-30 17:54:00                                DOG TRAPPED UNDER CAR
2021-01-31 12:53:00                   CAT STUCK UP TREE - RSPCA ON SCENE
2021-01-31 13:48:00           INJURED CAT STUCK IN GREEN AREA AT REAR OF
Name: FinalDescription, dtype: object
In [12]:
# resample the timeseries by month and count incidents
df.resample("M")["IncidentNumber"].count().plot(title="Monthly Calls")
# see https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
plt.show()
No description has been provided for this image
In [13]:
# resample by year, sum total costs, average hourly costs
fig, axs = plt.subplots(figsize=(16, 4), ncols=2)
df.resample("Y")["IncidentNotionalCost(£)"].sum().plot(
    title="Year total cost", ax=axs[0]
)
df.resample("Y")["HourlyNotionalCost(£)"].mean().plot(
    title="Average hourly cost", ax=axs[1]
)
plt.show()
No description has been provided for this image

Missing data¶

Different strategies for dealing with missing data:

  • Ignore the issue
    • some things may break / not work as expected
  • Remove rows/columns with missing data
    • remove all rows with missing data: df.dropna(axis=0)
    • remove all columns with missing data: df.dropna(axis=1)
  • Guess (impute) missing data
    • replace all missing entries with a value: df.fillna(1)
    • replace missing entries with mean for that column df.fillna(df.mean())
    • replace each missing entry with previous valid entry: df.fillna(method="pad")
    • replace missing by interpolating between valid entries: df.interpolate()
In [14]:
# count missing entries for each column
df.isna().sum()
Out[14]:
IncidentNumber                   0
CalYear                          0
FinYear                          0
TypeOfIncident                   0
PumpCount                       65
PumpHoursTotal                  66
HourlyNotionalCost(£)            0
IncidentNotionalCost(£)         66
FinalDescription                 5
AnimalGroupParent                0
OriginofCall                     0
PropertyType                     0
PropertyCategory                 0
SpecialServiceTypeCategory       0
SpecialServiceType               0
WardCode                        10
Ward                            10
BoroughCode                     12
Borough                         12
StnGroundName                    0
UPRN                          6127
Street                           0
USRN                          1156
PostcodeDistrict                 0
Easting_m                     5108
Northing_m                    5108
Easting_rounded                  0
Northing_rounded                 0
Latitude                      5108
Longitude                     5108
dtype: int64
In [15]:
# If PumpCount is missing, typically so is PumpHoursTotal
# 66 rows are missing at least one of these
pump_missing = df["PumpCount"].isna() | df["PumpHoursTotal"].isna()
print(pump_missing.sum())

66
In [16]:
# so we could choose to drop these rows
df1 = df.drop(df.loc[pump_missing == True].index)
# here we made a new dataset df1 with these rows dropped
# to drop the rows from the original dataset df, could do:
#
# df = df.drop(df.loc[pump_missing == True].index)
#
# or:
#
# df.drop(df.loc[pump_missing == True].index, inplace=True)
#
print(len(df1))

9662
In [17]:
# another equivalent way to do this
df2 = df.dropna(subset=["PumpCount", "PumpHoursTotal"])
print(len(df2))

9662
In [18]:
# but if we drop them, we lose valid data from other columns
# let's look at the distribution of values:
fig, axs = plt.subplots(1, 2, figsize=(14, 6))
df.plot.hist(y="PumpCount", ax=axs[0])
df.plot.hist(y="PumpHoursTotal", ax=axs[1])
plt.plot()
Out[18]:
[]
No description has been provided for this image
In [19]:
# looks like it would be better to replace missing PumpCount and PumpHoursTotal fields with 1
?df.fillna
df.fillna({"PumpCount": 1, "PumpHoursTotal": 1}, inplace=True)
In [20]:
df.isna().sum()
Out[20]:
IncidentNumber                   0
CalYear                          0
FinYear                          0
TypeOfIncident                   0
PumpCount                        0
PumpHoursTotal                   0
HourlyNotionalCost(£)            0
IncidentNotionalCost(£)         66
FinalDescription                 5
AnimalGroupParent                0
OriginofCall                     0
PropertyType                     0
PropertyCategory                 0
SpecialServiceTypeCategory       0
SpecialServiceType               0
WardCode                        10
Ward                            10
BoroughCode                     12
Borough                         12
StnGroundName                    0
UPRN                          6127
Street                           0
USRN                          1156
PostcodeDistrict                 0
Easting_m                     5108
Northing_m                    5108
Easting_rounded                  0
Northing_rounded                 0
Latitude                      5108
Longitude                     5108
dtype: int64

Count the unique entries in each column¶

In [21]:
df.nunique().sort_values()
Out[21]:
TypeOfIncident                   1
PumpCount                        4
SpecialServiceTypeCategory       4
PropertyCategory                 7
OriginofCall                     8
PumpHoursTotal                  12
HourlyNotionalCost(£)           13
CalYear                         15
FinYear                         16
SpecialServiceType              24
AnimalGroupParent               28
BoroughCode                     37
Borough                         70
IncidentNotionalCost(£)         82
StnGroundName                  108
PropertyType                   187
PostcodeDistrict               277
Northing_rounded               425
Easting_rounded                530
WardCode                       759
Ward                          1272
UPRN                          3446
Northing_m                    4188
Easting_m                     4254
Longitude                     4549
Latitude                      4549
FinalDescription              5907
USRN                          6496
Street                        7172
IncidentNumber                9728
dtype: int64
In [22]:
# "cat" and "Cat" are treated as different animals here:
df["AnimalGroupParent"].unique()
Out[22]:
array(['Dog', 'Fox', 'Horse', 'Rabbit',
       'Unknown - Heavy Livestock Animal', 'Squirrel', 'Cat', 'Bird',
       'Unknown - Domestic Animal Or Pet', 'Sheep', 'Deer',
       'Unknown - Wild Animal', 'Snake', 'Lizard', 'Hedgehog', 'cat',
       'Hamster', 'Lamb', 'Fish', 'Bull', 'Cow', 'Ferret', 'Budgie',
       'Unknown - Animal rescue from water - Farm animal', 'Pigeon',
       'Goat', 'Tortoise',
       'Unknown - Animal rescue from below ground - Farm animal'],
      dtype=object)
In [23]:
# select rows where AnimalGroupParent is "cat", replace with "Cat"
df.loc[df["AnimalGroupParent"] == "cat", "AnimalGroupParent"] = "Cat"
In [24]:
df["AnimalGroupParent"].unique()
Out[24]:
array(['Dog', 'Fox', 'Horse', 'Rabbit',
       'Unknown - Heavy Livestock Animal', 'Squirrel', 'Cat', 'Bird',
       'Unknown - Domestic Animal Or Pet', 'Sheep', 'Deer',
       'Unknown - Wild Animal', 'Snake', 'Lizard', 'Hedgehog', 'Hamster',
       'Lamb', 'Fish', 'Bull', 'Cow', 'Ferret', 'Budgie',
       'Unknown - Animal rescue from water - Farm animal', 'Pigeon',
       'Goat', 'Tortoise',
       'Unknown - Animal rescue from below ground - Farm animal'],
      dtype=object)
In [25]:
df.groupby("AnimalGroupParent")["IncidentNumber"].count().sort_values().plot.barh(
    logx=True
)
plt.show()
No description has been provided for this image
In [26]:
# apparently different hourly costs
# does it depend on the type of event? or does it just increase over time?
df["HourlyNotionalCost(£)"].unique()
Out[26]:
array([255, 260, 290, 295, 298, 326, 328, 333, 339, 346, 352, 364, 388])
In [27]:
# just goes up over time
df["HourlyNotionalCost(£)"].plot.line()
Out[27]:
<Axes: xlabel='DateTimeOfCall'>
No description has been provided for this image
In [28]:
# Group incidents by fire station & count them
df.groupby("StnGroundName")["IncidentNumber"].count()
Out[28]:
StnGroundName
Acton          74
Addington      66
Barking        91
Barnet         95
Battersea      82
               ..
Whitechapel    26
Willesden      68
Wimbledon      75
Woodford       95
Woodside       83
Name: IncidentNumber, Length: 108, dtype: int64

Plot location of calls on a map¶

  • note: this section uses some more libraries, to install them:
  • pip install geopandas contextily
In [29]:
# drop missing longitude/latitude
df2 = df.dropna(subset=["Longitude", "Latitude"])
# also drop zero values
df2 = df2[df2["Latitude"] != 0]
# convert to geodataframe using geopandas
import geopandas

# set crs to EPSG:4326 to specify WGS84 Latitude/Longitude
gdf = geopandas.GeoDataFrame(
    df2,
    geometry=geopandas.points_from_xy(df2["Longitude"], df2["Latitude"]),
    crs="EPSG:4326",
)
gdf.head()
Out[29]:
IncidentNumber CalYear FinYear TypeOfIncident PumpCount PumpHoursTotal HourlyNotionalCost(£) IncidentNotionalCost(£) FinalDescription AnimalGroupParent ... Street USRN PostcodeDistrict Easting_m Northing_m Easting_rounded Northing_rounded Latitude Longitude geometry
DateTimeOfCall
2009-01-01 08:51:00 275091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Fox ... Grasmere Road NaN SE25 534785.0 167546.0 534750 167550 51.390954 -0.064167 POINT (-0.06417 51.39095)
2009-01-04 10:07:00 2075091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Dog ... Mill Lane NaN SM5 528041.0 164923.0 528050 164950 51.368941 -0.161985 POINT (-0.16199 51.36894)
2009-01-05 12:27:00 2872091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Horse ... Park Lane 21401484.0 UB9 504689.0 190685.0 504650 190650 51.605283 -0.489684 POINT (-0.48968 51.60528)
2009-01-07 06:29:00 4011091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Dog ... Holloway Road NaN E11 539013.0 186162.0 539050 186150 51.557221 0.003880 POINT (0.00388 51.55722)
2009-01-07 11:55:00 4211091 2009 2008/09 Special Service 1.0 1.0 255 255.0 Redacted Dog ... Aldersbrook Road NaN E12 541327.0 186654.0 541350 186650 51.561067 0.037434 POINT (0.03743 51.56107)

5 rows × 31 columns

In [30]:
f, ax = plt.subplots(figsize=(16, 16))
# plot location of calls involving animals
gdf.plot(ax=ax, color="black", alpha=0.3)
plt.title("Call locations")
# plt.axis("off")
plt.show()
No description has been provided for this image
In [31]:
import contextily as cx

f, ax = plt.subplots(figsize=(16, 16))
# plot location of calls involving animals
gdf.plot(ax=ax, color="black", alpha=0.3)
# add a basemap of the region using contextily
cx.add_basemap(ax, crs=gdf.crs)
plt.title("Call locations")
plt.axis("off")
plt.show()
No description has been provided for this image
In [32]:
f, ax = plt.subplots(figsize=(16, 16))
# plot location of calls involving animals
for animal, colour in [
    ("Cow", "black"),
    ("Deer", "red"),
    ("Fox", "blue"),
    ("Snake", "yellow"),
]:
    gdf[gdf["AnimalGroupParent"] == animal].plot(
        ax=ax, color=colour, alpha=0.5, label=animal
    )
# add a basemap of the region using contextily
cx.add_basemap(ax, crs=gdf.crs)
plt.title("Call locations by animal")
plt.legend()
plt.axis("off")
plt.show()
No description has been provided for this image

Next steps¶

  • experiment with your own datasets
  • read some pandas documentation
    • user guide
  • follow a tutorial
    • getting started tutorials
  • free interactive kaggle courses
    • pandas
    • data cleaning